@ARTICLE{Diamond2013-uo,
  title     = "Genetic Matching for Estimating Causal Effects: A General
               Multivariate Matching Method for Achieving Balance in
               Observational Studies",
  author    = "Diamond, Alexis and Sekhon, Jasjeet S",
  abstract  = "Abstract This paper presents genetic matching, a method of
               multivariate matching that uses an evolutionary search algorithm
               to determine the weight each covariate is given. Both propensity
               score matching and matching based on Mahalanobis distance are
               limiting cases of this method. The algorithm makes transparent
               certain issues that all matching methods must confront. We
               present simulation studies that show that the algorithm improves
               covariate balance and that it may reduce bias if the selection
               on observables assumption holds. We then present a reanalysis of
               a number of data sets in the LaLonde (1986) controversy.",
  journal   = "Rev. Econ. Stat.",
  publisher = "MIT Press",
  volume    =  95,
  number    =  3,
  pages     = "932--945",
  month     =  jul,
  year      =  2013
}

@ARTICLE{Sekhon1998-pw,
  title     = "Genetic Optimization Using Derivatives",
  author    = "Sekhon, Jasjeet S and Mebane, Walter R",
  abstract  = "[We describe a new computer program that combines evolutionary
               algorithm methods with a derivative-based, quasi-Newton method
               to solve difficult unconstrained optimization problems. The
               program, called GENOUD (GENetic Optimization Using Derivatives),
               effectively solves problems that are nonlinear or perhaps even
               discontinuous in the parameters of the function to be optimized.
               When a statistical model's estimating function (for example, a
               log-likelihood) is nonlinear in the model's parameters, the
               function to be optimized will usually not be globally concave
               and may contain irregularities such as saddlepoints or
               discontinuous jumps. Optimization methods that rely on
               derivatives of the objective function may be unable to find any
               optimum at all. Or multiple local optima may exist, so that
               there is no guarantee that a derivative-based method will
               converge to the global optimum. We discuss the theoretical basis
               for expecting GENOUD to have a high probability of finding
               global optima. We conduct Monte Carlo experiments using scalar
               Normal mixture densities to illustrate this capability. We also
               use a system of four simultaneous nonlinear equations that has
               many parameters and multiple local optima to compare the
               performance of GENOUD to that of the Gauss-Newton algorithm in
               SAS's PROC MODEL.]",
  journal   = "Polit. Anal.",
  publisher = "[Oxford University Press, Society for Political Methodology]",
  volume    =  7,
  pages     = "187--210",
  year      =  1998
}


@article{Sekhon.2008,
  title={Multivariate and propensity score matching software with automated balance optimization: the matching package for R},
  author={Sekhon, Jasjeet S},
  journal={Journal of Statistical Software},
  year={2008}, 
  volume = {47}, 
  issue = {7}, 
  pages= {1-52}
}


@incollection{Rossteuscher.2017,
  title = {Candidate perception and individual vote choice}, 
  author = {Ro{\ss}teutscher, Sigrid and Bieber, Ina and St{\"o}vsand, Lars-Christopher and Blumenberg, Manuela},
  booktitle= {Voters and voting in context: Multiple contexts and the heterogeneous German electorate},
  editors = {Schoen, Harald and Schmitt-Beck, R{\"u}diger},
  year={2017},
  publisher={Oxford University Press}, 
  pages = {190 - 208}
}



@article{Gorecki.2012,
  title={Not just ‘friends and neighbours’: Canvassing, geographic proximity and voter choice},
  author={G{\'o}recki, Maciej A and Marsh, Michael},
  journal={European Journal of Political Research},
  volume={51},
  number={5},
  pages={563--582},
  year={2012},
  publisher={Wiley Online Library}
}

@article{Papke.1996,
  title={Econometric methods for fractional response variables with an application to 401 (k) plan participation rates},
  author={Papke, Leslie E and Wooldridge, Jeffrey M},
  journal={Journal of Applied Econometrics},
  volume={11},
  number={6},
  pages={619--632},
  year={1996},
  publisher={Wiley Online Library}
}